Comment by ianjbutler
4 hours ago
> These researchers wanted methods based on human input to win and were disappointed when they did not.[1]
This was/is basically a strawman though. Like maybe "human input winning" was desirable for chess masters but for computer science wonks? Not the point or the disappoint. It's always neats and scruffies fighting about using some kind of recognizable method (logic) instead of magic (ML).
> breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.
More to OP's point I think: nowadays when someone wants to beat you over the head with the bitter lesson, they aren't as careful to include learning and search. They want to say learning leads to intuition (magic) whereby we can avoid work (logic/search), and maybe argue or assume from there that neats and scruffies is settled. TBF, something like reasoning in latent space does resemble intuition!
But the real lesson is confirmed every time we bother to check, and not very bitter for anyone. Search/learning/logic are ALL always necessary on any sufficiently difficult problems, and hybrids that interleave always outperform everything else. Stockfish being the example in this thread that different camps of absolutists would like to claim, but also all the MCTS examples, evolving examples, and new hybrids all the time. My favorite lately: https://arxiv.org/pdf/2511.08983
> This was/is basically a strawman though. Like maybe "human input winning" was desirable for chess masters but for computer science wonks?
Oh no!
The whole field was full of people whose entire career was built around the idea of developing smart priors.
To quote Wikipedia:
> For computer vision in particular, much progress came from manual feature engineering, such as SIFT features, SURF features, HoG features, bags of visual words, etc. It was a minority position in computer vision that features can be learned directly from data
This undersells the change though! David Lowe's reputation as the best image researcher in the world was based on his SIFT patent[1]
This approach worked until 30 September 2012.
That was a bitter day for many, many computer science researchers.
[1] https://en.wikipedia.org/wiki/Scale-invariant_feature_transf...
Is being wrong/ignorant about whether/how something can be automated the same as having a preference for doing it manually? Maybe so if it's your patent, your thesis I guess..
But as it relates to more/less magic, maybe the more modern lens on this is e.g. https://arxiv.org/html/2505.11581v1 . Is manual feature-engineering more like what you'd evolve, or more like what you'd get from SGD ? Feasibility and performance is always a question, there are others like what is robust, stable, adaptable, predictable, explainable. Maybe the manual-features people were interested in something besides the manual part? Maybe the story isn't so simple, and maybe it's not finished yet.